Allocation system setup optimization in a cost-benefit perspective

CMR has developed an in-house framework for efficient and accurate modelling of allocation systems, uncertainty calculations and optimization/risk minimization, combining longstanding in-depth domain knowledge of flow metering and state-of-the-art computational methods. The developed approach is fully compliant with the ISO Guide to the expression of uncertainty in measurement (ISO GUM).

Allocation system setup optimization in a cost-benefit perspective

ABSTRACT: Allocation of hydrocarbons to their original production sources, also known as hydrocarbon accounting, is a key factor for the distribution of costs, revenues and taxes between interested parties in field development and production of oil and gas. When developing an allocation system, the allocation uncertainties in the system should be understood and accepted by all involved parties. Furthermore, the implemented allocation system should be cost efficient and practical to operate. One of the pivotal design questions for such an allocation system is the choice of measurement uncertainty of the individual metering stations comprizing the system. In this paper, we device a framework for allocation system modeling that allows for an algorithmic solution to the problem of optimizing the allocation system setup, i.e., choosing the right meter with the right uncertainty at the right place. This includes balancing the risk associated with misallocation due to measurement uncertainty against the cost of realizing the system. The presented framework makes use of a combination of optimization and ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM) compliant Monte Carlo simulations. We illustrate the usefulness of our framework by applying it to example allocation systems with different allocation principles and production rates. We review the obtained results and provide a discussion of strengths and current limitations of the proposed approach.

The main contributions of this paper are 1) a novel framework for determining the optimal allocation system setup, 2) a flexible mathematical model for allocation systems, and 3) a description of a practical implementation of the framework as a coupling between Monte Carlo simulations and an optimization routine.

DOI: 10.1016/j.petrol.2016.08.025